118 citations · 386 across the 14 of their papers we have counts for
14 papers
ARCA23K: An audio dataset for investigating open-set label noise
Turab Iqbal, Yin Cao, Andrew Bailey +2
The availability of audio data on sound sharing platforms such as Freesound gives users access to large amounts of annotated audio. Utilising such data for training is becoming inc…
Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning
Xubo Liu, Turab Iqbal, Jinzheng Zhao +3
Deep generative models have recently achieved impressive performance in speech and music synthesis. However, compared to the generation of those domain-specific sounds, generating…
Enhancing Audio Augmentation Methods with Consistency Learning
Turab Iqbal, Karim Helwani, Arvindh Krishnaswamy +1
Data augmentation is an inexpensive way to increase training data diversity and is commonly achieved via transformations of existing data. For tasks such as classification, there i…
An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection
Yin Cao, Turab Iqbal, Qiuqiang Kong +3
Polyphonic sound event localization and detection (SELD), which jointly performs sound event detection (SED) and direction-of-arrival (DoA) estimation, detects the type and occurre…
Event-Independent Network for Polyphonic Sound Event Localization and Detection
Yin Cao, Turab Iqbal, Qiuqiang Kong +3
Polyphonic sound event localization and detection is not only detecting what sound events are happening but localizing corresponding sound sources. This series of tasks was first i…
Learning with Out-of-Distribution Data for Audio Classification
Turab Iqbal, Yin Cao, Qiuqiang Kong +2
In supervised machine learning, the assumption that training data is labelled correctly is not always satisfied. In this paper, we investigate an instance of labelling error for cl…
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
Qiuqiang Kong, Yin Cao, Turab Iqbal +3
Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classi…
Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy
Yin Cao, Qiuqiang Kong, Turab Iqbal +3
Sound event detection (SED) and localization refer to recognizing sound events and estimating their spatial and temporal locations. Using neural networks has become the prevailing…
Cross-task learning for audio tagging, sound event detection spatial localization: DCASE 2019 baseline systems
Qiuqiang Kong, Yin Cao, Turab Iqbal +3
The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge focuses on audio tagging, sound event detection and spatial localisation. DCASE 2019 consists…
Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems
Qiuqiang Kong, Yin Cao, Turab Iqbal +3
The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge focuses on audio tagging, sound event detection and spatial localisation. DCASE 2019 consists…
Weakly Labelled AudioSet Tagging with Attention Neural Networks
Qiuqiang Kong, Changsong Yu, Turab Iqbal +3
Audio tagging is the task of predicting the presence or absence of sound classes within an audio clip. Previous work in audio tagging focused on relatively small datasets limited t…
DCASE 2018 Challenge Surrey Cross-Task convolutional neural network baseline
Qiuqiang Kong, Turab Iqbal, Yong Xu +2
The Detection and Classification of Acoustic Scenes and Events (DCASE) consists of five audio classification and sound event detection tasks: 1) Acoustic scene classification, 2) G…
Capsule Routing for Sound Event Detection
Turab Iqbal, Yong Xu, Qiuqiang Kong +1
The detection of acoustic scenes is a challenging problem in which environmental sound events must be detected from a given audio signal. This includes classifying the events as we…
Approximate Message Passing for Underdetermined Audio Source Separation
Turab Iqbal, Wenwu Wang
Approximate message passing (AMP) algorithms have shown great promise in sparse signal reconstruction due to their low computational requirements and fast convergence to an exact s…